Emerging multimedia Multiview video systems consist of a dense deployment of multiple partial-overlapped wireless cameras, as well as some access points (Aps) and many wireless distributed relay nodes. Correlated views are captured by cameras followed being transmitted to destination by different Aps and networks links. Packet expiration of one camera flow may harm the whole task. To effectively integrate multiple viewpoints into a whole image, the correlated data rate and deadline of flows from multiple cameras are meaningful. There is a trade-off between data redundancy and time deadline among correlated multi-views subjecting to the constraints of limited buffer length. However, most researches in this field have not considered packet expiration suffering from varieties of delays after multipath. In this paper, we conduct this problem to optimally adjust multiple flows of viewpoints by exploiting spatial and temporal correlations among cameras to reduce delay variances. A global optimization algorithm based on joint rate-distortion and delay-distortion model is proposed. Simulation results show that quality of service for Multiview streaming can be improved by allocating suitable transmission rates among correlated cameras as well as appropriate playout deadline. The PSNR quality shows that better performance can be achieved compared with baseline policies.


Correlation Multiview streaming Packet scheduling Delay 



This work was supported by the National Science Foundation of China under Grant (No. 61202470, No. 61471271), Wuhan Science and Technology Project (No.2013010501010148, No. 2014010202010108), Chin Postdoctoral Science Foundation (No. 2013M531711), Financially supported by self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (No.CCNU14A05016, No. CCNU15A02017).


  1. 1.
    Chou, P., Miao, Z.: Rate-distortion optimized streaming of packetized media. IEEE Trans. Multimed. 8(2), 390–404 (2006)CrossRefGoogle Scholar
  2. 2.
    Fu, F., van der Schaar, M.: Structural solution for dynamic scheduling in wireless multimedia transmission. IEEE Trans. Circ. Syst. Video Technol. 22(5), 727–739 (2012)CrossRefGoogle Scholar
  3. 3.
    Wang, P., Dai, R., Akyildiz, I.F.: Visual correlation-based image gathering for wireless multimedia sensor networks. In: IEEE Proceedings INFOCOM, pp. 746–749. IEEE Press (2011)Google Scholar
  4. 4.
    Chakareski, J.: Transmission policy selection for multi-view content delivery over bandwidth constrained channels. IEEE Trans. Image Process. 23(2), 931–942 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Toni, L., Maugey, T., Frossard, P.: Correlation-aware packet scheduling in multi-camera networks. IEEE Trans. Multimed. 16(2), 496–509 (2014)CrossRefGoogle Scholar
  6. 6.
    Kurutepe, E., Civanlar, M.R., Tekalp, A.M.: Client-driven selective streaming of multiview video for interactive 3DTV. IEEE Trans. Circ. Syst. Video Technol. 17(11), 1558–1565 (2007)CrossRefGoogle Scholar
  7. 7.
    De Abreu, A., Frossard, P., Pereira, F.: Optimizing multiview video plus depth prediction structures for interactive multiview video streaming. IEEE J. Sel. Topics Signal Process. 9(3), 487–500 (2015)CrossRefGoogle Scholar
  8. 8.
    Chakareski, J: Scheduling space-time dependent packets in multi-view video streaming. In: IEEE 15th International Workshop on Multimedia Signal Processing (MMSP), pp. 070–075 (2013)Google Scholar
  9. 9.
    Cheung, G., Ortega, V., Cheung, N.-M.: Interactive streaming of stored multiview video using redundant frame structures. IEEE Trans. Image Process. 20(3), 744–761 (2011)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Lou, J.-G., Cai, H., Li, J.: Interactive multiview video delivery based on IP multicast. J. Adv. Multimed. 2007(1), 13 (2007)Google Scholar
  11. 11.
    Li, Z., Begen, A.C., Gahm, J., Shan, Y., Osler, B., Oran, D.: Streaming video over HTTP with consistent quality. In: Proceedings of ACM Multimedia System Conference, pp. 248–258 (2014)Google Scholar
  12. 12.
    Cheung, G., Ortega, A., Cheung, N.: Interactive streaming of stored multiview video using redundant frame structures. IEEE Trans. Image Process. 3(3), 744–761 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Stuhlmuler, K., Faber, N., Link, M., Girod, B.: Analysis of video transmission over lossy channels. IEEE J. Sel. Areas Commu. 18(6), 1012–1032 (2000)CrossRefGoogle Scholar
  14. 14.
    Guan, Z., Melodia, T., Yuan, D.: Joint optimal rate control and relay selection for cooperative wireless video streaming. IEEE/ACM Trans. Netw. 21(4), 1173–1186 (2013)CrossRefGoogle Scholar
  15. 15.
    Dou, Y., Zeng, K.C., Yang, Y., Yao, D.D.: MadeCR: correlation-based malware detection for cognitive radio. In: IEEE Conference on Computer Communications INFOCOM (2015)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  1. 1.School of Computer ScienceHuazhong Normal UniversityWuhanChina
  2. 2.School of Computer ScienceWuhan UniversityWuhanChina

Personalised recommendations